IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2021-20.html
   My bibliography  Save this paper

Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis

Author

Listed:
  • Sayani Gupta
  • Rob J Hyndman
  • Dianne Cook

Abstract

Cyclic temporal granularities are temporal deconstructions of a time period into units such as hour-of-theday and work-day/weekend. They can be useful for measuring repetitive patterns in large univariate time series data, and feed new approaches to exploring time series data. One use is to take pairs of granularities, and make plots of response values across the categories induced by the temporal deconstruction. However, when there are many granularities that can be constructed for a time period, there will also be too many possible displays to decide which might be the more interesting to display. This work proposes a new distance metric to screen and rank the possible granularities, and hence choose the most interesting ones to plot. The distance measure is computed for a single or pairs of cyclic granularities and can be compared across different cyclic granularities or on a collection of time series. The methods are implemented in the open-source R package hakear.

Suggested Citation

  • Sayani Gupta & Rob J Hyndman & Dianne Cook, 2021. "Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis," Monash Econometrics and Business Statistics Working Papers 20/21, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2021-20
    as

    Download full text from publisher

    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp20-2021.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahbubul Majumder & Heike Hofmann & Dianne Cook, 2013. "Validation of Visual Statistical Inference, Applied to Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 942-956, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2023. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(3), pages 1977-2019.
    2. Paul Blanche & Thomas A. Gerds & Claus T. Ekstrøm, 2019. "The Wally plot approach to assess the calibration of clinical prediction models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 150-167, January.
    3. Andrew Zammit‐Mangion, 2020. "Discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    4. Niladri Roy Chowdhury & Dianne Cook & Heike Hofmann & Mahbubul Majumder & Eun-Kyung Lee & Amy Toth, 2015. "Using visual statistical inference to better understand random class separations in high dimension, low sample size data," Computational Statistics, Springer, vol. 30(2), pages 293-316, June.
    5. Jakob Peterlin & Nataša Kejžar & Rok Blagus, 2023. "Correct specification of design matrices in linear mixed effects models: tests with graphical representation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 184-210, March.

    More about this item

    Keywords

    data visualization; cyclic granularities; periodicities; permutation tests; distributional difference; Jensen-Shannon distances; smart meter data; R;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2021-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.